Auto defect screening using adaptive machine learning in semiconductor device manufacturing flow

ABSTRACT

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to semiconductor devicefabrication, and more specifically to auto defect screening in themanufacturing flow of fabricating semiconductor devices.

2. Description of Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Semiconductor devices are manufactured by fabricating many layers ofcircuit patterns on wafers to form a massive number of transistors forintegration as complicated circuits. In the manufacturing flow ofsemiconductor devices, lithographic process (LP) is responsible fortransferring circuit patterns created by circuit designers onto wafers.

Photomasks/reticles with opaque and clear patterns according to thecircuit patterns are used for patterning device layers on wafers.Distortion of the patterns can result from the effect of the neighboringpatterns on the photomask and optical diffraction, photoresistdevelopment and etching, chemical-mechanical polishing (CMP) on adjacentlayers of the wafer, and geometric and overlaying relationships betweenpatterns of adjacent layers fabricated on the wafer. As the componentdensity of the integrated circuits (ICs) has increased the complexity ofthe IC patterns and layouts, systematic defects resulting fromdistortion of patterns or random defects resulting from processvariation or contamination can fail the device fabricated on the wafer.

Wafer inspections on various patterned layers are routinely adopted inthe production flow of manufacturing semiconductor devices. Opticalinspection that has throughput of more than one full wafer per hour isthe major work force in wafer inspection. In a typical wafer inspection,defects are detected along with nuisances which may be false alarms ordefects of no interests. As the design rule shrinks, the sizes of manycritical defects are also smaller and the signals of defects becomeweaker in comparison to signals of noise and normal process variation.As a result, a huge number of nuisances are often reported before asmall number of critical defects of interest can be detected in theadvanced technology nodes. It is a challenge for semiconductor devicemanufacturers to identify those critical defects of interest during bothramp-up and mass production periods of the manufacturing process.

In an optical inspection tool, nuisance filtering technique has beenprovided in a more advanced inspection recipe to help reduce the numberof nuisances. In order to take advantage of the nuisance filteringtechnique, users have to carefully analyze and review the inspectionresults collected from one or more wafers using various defect analysistools or a scanning electron microscope (SEM) review station to labeleach defect candidate as being a real defect or nuisance. The labelledreal defects and nuisances are used to generate a nuisance filter. Thenuisance filter is then included in the advanced recipe of theinspection tool to filter out the nuisances.

As the device technology advances to 20 nm and below, in order to retaincritical defects of interest, the number of nuisances detected in thewafer inspection usually represents more than 90% of the reporteddefects from an optical inspection tool even after the nuisancefiltering technique has been applied. The performance of the nuisancefiltering technique cannot achieve the desired result of effectivelyfiltering out the nuisances for several reasons.

One is that it is practically impossible to collect adequate criticaldefect types for generating the nuisance filter from a small number ofinspected wafers. Another reason is that optical patches collected forinspection cannot resolve circuit patterns and can only provide verylimited information at the advanced technology nodes. In addition, themassive amount of data that an inspection tool has to process in orderto meet the required high throughput also limit the complexity of theaffordable computation of the nuisance filter in the inspection.Furthermore, the continuing variation in the process window may alsochange the behavior of the nuisances and trigger new defect types thatmake the nuisance filer obsolete and not effective. As a result, theinspection tool still has to output a large number of nuisances in orderto not miss critical defects of interest.

Therefore, during the ramp-up period, a large number of engineers andoperators are dedicated to visually review the inspection result usingSEM review tool in order to screen out the critical defects of interestto diagnose and improve the yield of the manufacturing process. Duringthe mass production period, a small number of defects are usuallysampled for SEM review to control the manufacturing process assumingthat most of critical defects have been eliminated in the ramp-upperiod. As a result, there is significant risk for the semiconductordevice manufactures to discover unknown critical defects only after theyield of the manufactured semiconductor device has been significantlyimpacted.

SUMMARY OF THE INVENTION

The present invention has been made to overcome the above mentionedchallenges and difficulties in screening out critical defects ofinterest in wafer inspection for the semiconductor device manufacturingprocess. Accordingly, the present invention provides a system and methodfor auto defect screening in the semiconductor device manufacturing flowusing adaptive machine learning.

The system for the adaptive machine learning according to the presentinvention comprises an adaptive model controller, a defect/nuisancelibrary and a module for executing data modeling analytics. The adaptivemodel controller receives data from feed-forward and feedback paths inthe semiconductor device manufacturing flow, interfaces with SEMreview/inspection, updates the defect/nuisance library, compiles andsends model training data and model validation data for executing thedata modeling analytics.

The adaptive model controller includes a defect sampler, a SEM interfaceand a training data and model manager. The defect sampler receives aplurality of defect candidates in wafer inspection from the feed-forwardpath and defects of interest that have already been screened by one ormore existing defect screening models after wafer inspection from thefeedback path.

The defect sampler sends data samples including sampled defectcandidates and defects of interest to the SEM interface thatcommunicates with SEM review/inspection to acquire SEM resultscorresponding to the data samples. Each data sample is validated andlabelled as being a real defect or nuisance in the corresponding SEMresults.

The training data and model manager receives the data samples andcorresponding SEM results, establishes and updates a defect/nuisancelibrary, and compiles model training data and model validation data forthe data modeling analytics to generate a data model as the defectscreening model for auto defect screening. In the initial phase of theauto defect screening, the system executes a few continuous iterationsof the adaptive machine learning to establish one or more defectscreening models based on a target specification. The defect samplercontinues to receive feedforward and feedback data for the training dataand model manager to control when the defect screening models should beupdated by the data modeling analytics.

The present invention further provides a critical signature library thatinterfaces with the data modeling analytics to perform criticalsignature analytics and generate critical signature models for autodefect screening. The critical signature library includes a plurality ofcritical signature databases. Each critical signature data base stores anumber of critical circuit patterns, the associated design clips, defectfeatures, optical patches, and SEM images, and the correspondingcritical signature models.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be apparent to those skilled in the art byreading the following detailed description of preferred embodimentsthereof, with reference to the attached drawings, in which:

FIG. 1 shows a block diagram of auto defect screening using adaptivemachine learning in the manufacturing flow of fabricating semiconductordevices according the present invention;

FIG. 2 shows the block diagram of a system for performing the adaptivemachine learning for auto defect screening according to the presentinvention;

FIG. 3 shows the block diagram of the adaptive model controller in theadaptive machine learning according to the present invention;

FIG. 4 shows that auto defect screening using adaptive machine learningfurther interfaces with a critical signature library according to thepresent invention;

FIG. 5 shows that the adaptive machine learning generates criticalsignature models using critical signature analytics with reference tothe critical signature library;

FIG. 6 shows that the critical signature library includes a number ofcritical signature data bases; and

FIG. 7 shows a flow chart of the method for performing the adaptivemachine learning for auto defect screening according to the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows an embodiment of auto defect screening using adaptivemachine learning in the semiconductor device manufacturing flowaccording to the present invention. With reference to FIG. 1, waferinspection 101 is routinely performed in semiconductor devicemanufacturing for identifying defect candidates 102. In an inline waferinspection, the inspected area usually covers a full wafer orsignificant portions of the full wafer. Although using design data ofthe manufactured semiconductor device in a wafer inspection is optional,the design data are provided more and more to help the wafer inspectionachieve higher sensitivity as well as more accurate inspected areas.

Die-to-die optical inspection is most widely used for wafer inspection.Optical images of dies with high resolution are scanned for comparisonand detecting defects. In an advanced technology node, an opticalinspection tool with inspection pixel sizes in the order of 30 to 50 nmis typically used because the fast throughput of optical inspection canachieve the speed of more than one full wafer per hour. E-beaminspection tools may provide higher sensitivity for hot spot inspection.However, their throughput remains too slow for inline full waferinspection.

The output of the wafer inspection is a list of defect candidates 102.Each defect candidate is reported with its coordinate, bounding box,size and other features that the inspection tool determines and extractsfrom the optical images. As pointed out earlier, a huge number of defectcandidates 102 are often reported from the wafer inspection as thedesign rule of the semiconductor device shrinks. It is not unusual thatmore than 90 percent of the defect candidates 102 are nuisances or falsealarms in the advanced technology nodes. The challenge to thesemiconductor device manufacturers is how to screen out the real defectsof interest from the huge amount of defect candidates to diagnosecritical yield limiting problems in process ramp-up or perform routineprocess monitoring in mass production.

As pointed out earlier, although a nuisance filter may be provided in anadvanced inspection recipe to help reduce nuisances, the number ofdefect candidates 102 is still too large for process diagnosis inramp-up, and not effective for inline monitoring. As shown in FIG. 1,the present invention provides a method for auto defect screening 105 toscreen out defects of interest 106 based on adaptive machine learning104 with interface to SEM review/inspection 103 for acquiring SEMresults that validate real defects and nuisances. If the design data isavailable, design clips in the identified defective areas of the defectcandidates are cut from the design data for the adaptive machinelearning.

The recent advance in electron beam technology has shown that SEMreview/inspection can be performed with an image pixel size down to 1nm. Using such high resolution images in cooperation with advancedalgorithms, SEM review/inspection has proven to validate if a defectcandidate is real or nuisance with 95% accuracy although the throughputof SEM review/inspection is too low for full wafer inspections.

In order to perform the adaptive machine learning 104 of the presentinvention, both feed-forward and feed-back paths and provided to receivedefect candidates and defects of interest for SEM review/inspection tovalidate real defects as shown in FIG. 1. Defect candidates beforescreening are fed forward and defects of interest after screening arefed back for the machine learning technique to adaptively train andupdate a data model for auto defect screening.

Die-to-die SEM inspection by comparing die-to-die SEM images of thesampled defect candidates can be performed to acquire accurate SEMresults. As have been observed, many nuisances detected in opticalinspection due to interference effect caused by surface roughness orlayer thickness variation can be easily identified based on highresolution SEM images. Die-to-database SEM inspection by comparing SEMimages against the corresponding design clips can also be performed todetermine if the defect candidates are real or nuisance. More detailedclassification can further be performed based on analyzing the SEMimages and design clips.

According to the present invention, the SEM results with validated andlabelled real defects or nuisances acquired from SEM review/inspection103 and associated defect information such as defect features andoptical patches reported by the wafer inspection 101, and design clipscut from the design data are used in the adaptive machine learning 104.As shown in FIG. 2, the adaptive machine learning 104 includes adefect/nuisance library 200, an adaptive model controller 201 thatinterfaces with the defect/nuisance library 200 to store sampled defectcandidates as well as selected defects of interest that have beenvalidated by SEM review/inspection.

The adaptive model controller 201 includes a defect sampler 301, a SEMinterface 302 and a training data and model manager 303 as shown in FIG.3. The defect sampler 301 receives defect features, optical patches anddesign clips of the defect candidates in the feed-forward path or thedefects of interest in the feed-back path. Defect candidates are sampledso that the number of defect candidates after sampling is manageable bySEM review/inspection 103. The locations of sampled defect candidatesand defects of interest along with their corresponding design clips ifavailable are sent to SEM interface 302 that communicates with a SEMreview/inspection tool to acquire SEM results that validate and label ifthe sampled defect candidates or defects of interest are real defects ornuisances.

In the feed-forward path, the defect candidates 102 may be sparsely andrandomly sampled by the defect sampler 302 in the adaptive modelcontroller 201 if the number of defect candidates is too large. Othersampling strategies such as strategies based on the importance of careareas set up for inspecting the wafers or the pattern densities in thecorresponding design clips may also be adopted by the defect sampler301.

For example, if hot spots predicted by optical proximity correction(OPC) verification have been set up in the inspection for criticaldefect monitoring, defect candidates in the predicted hot spots may haveto be sampled more frequently by the defect sampler 301. Because defectsin blank areas may have no impact to the manufactured semiconductordevices, defect candidates in the blank area may be ignored. However,defects in areas of dense circuit patterns are likely to fail themanufactured semiconductor devices and it may be preferable to samplethem with higher priority.

Based on the SEM validated and labelled results, the training data andmodel manager 303 stores and updates the labelled data samples, whichinclude defect candidates and defects of interest but may be labelled asreal defects or nuisances, in the defect/nuisance library 200. It shouldbe noted that the defect/nuisance library 200 must include both realdefects and nuisances after SEM validation. The training data and modelmanager 303 further assigns a portion of the labelled data samples asmodel training data 202 and another portion of the labelled data samplesas model validation data 203 and initiates the execution of the datamodeling analytics 204 to generate one or more data models as defectscreening models 205.

During the initial phase of the adaptive machine learning 104, thepresent invention may sample and accumulate the defect candidates to setup the defect/nuisance library and perform the data modeling analytics204 shown in FIG. 2 through a few continuous iterations until a modeltarget specification has been satisfied by the generated defectscreening models 205.

In the generation of the defect screening models 205, the model targetspecification is set for the data modeling analytics 204 to validate theperformance of the generated defect screening models 205 based on themodel validation data 203. For example, the model target specificationmay be set based on percentages of accuracy and purity in terms of realdefects and nuisances predicted by the defect screening model 205 withthe model validation data 203.

In the field of machine learning, a number of features associated witheach data sample in the training data are typically used for trainingand generating the data model. The data modeling analytics 204 shown inFIG. 2 adopts similar principles and uses algorithms widely available inmachine learning.

According to the present invention, defect features reported by thewafer inspection are included as features for training and generatingthe defect screening models 205. Some other image features extractedfrom the optical patches of each data sample are also extracted.Examples of image features are maximum or minimum or average gray level,maximum or minimum or average gradient of the gray level of the pixelsin an optical patch image, or of the difference pixels between test andreference pixels of the optical patch images. In addition, a set offeatures are extracted from the design clips corresponding to the datasample. Examples of the features extracted from the design clips arepattern density, pattern perimeter, minimum or maximum linewidth,minimum or maximum spacing, pattern orientation, number of edges, insideor outside corners, spatial frequency distribution, . . . , etc. Thesefeatures described above are only examples and many others can beextracted based on specific interest.

With a target specification being set, a data model can be trained usingthe features extracted from each data sample in the model training data202. Many data model training algorithms have been widely used in dataanalysis and data mining of machine learning. For example, data modelingalgorithms are available based on decision tree, linear regression,nonlinear regression, support vector machine (SVM), k-Means clustering,hierarchical clustering, rule based, neuro network, . . . , etc. Allthose data model training algorithms can be applied to the modeltraining data 202 to establish a data model as a defect screening modelfor screening defects.

After a data model for the model training data 202 has been establishedas the defect screening model 205, the data model is applied to themodel validation data 203. The same sets of features are extracted foreach data sample in the model validation data 203. The defect screeningmodel 205 is used to test and predict each data sample in the modelvalidation data 203 as being a real defect or nuisance. The predictedresult is checked against the SEM results of the model validation data203 in the data modeling analytics 204 to verify if the targetspecification has been satisfied. If necessary, multiple models may begenerated by using different algorithms to meet the targetspecification.

In order to generate a stable and usable defect screening model 205,defect candidates that are representative enough to provide features forparametrically or statistically distinguishing real defects fromnuisances have to be fed to the data modeling analytics 204 in theadaptive machine learning 104. To achieve better defect screening,defect candidates sampled from inspecting a number of wafers may bepreferably based on priorities of care areas, predicted hot spot areas,pattern densities of circuit patterns, . . . , etc, as discussedearlier.

According to the adaptive machine learning of the present invention, thefeed-forward path shown in FIG. 1 provides the mechanism for acquiringsampled defect candidates including real defects and nuisances, and theSEM interface 302 provides the mechanism for validating and labelling ifthe defect candidates are real defects or nuisances. The feedback pathprovides the mechanism for acquiring defects of interest that havealready been screened to validate the effectiveness of the defectscreening model 205.

It can be understood that a defect screening model 205 may workeffectively if the data behavior of real defects and nuisances aresufficiently captured in the model training data 202. However, as thedesign rule shrinks, the process window becomes tighter. Processvariation may result in new defect types or alter the nature ofnuisances. In the present invention, the feed-forward path helps tocapture new defect types or nuisances with altered behavior, and thefeedback path helps to capture those nuisances that have not beenscreened out.

In accordance with the present invention, the training data and modelmanager 303 also determines how the defect candidates from thefeed-forward path and the defects of interest in the feedback pathshould be sampled or selected by the defect sampler 301 and used for thetraining data. For example, the defect candidates received from thefeed-forward path may be sampled uniformly and randomly across the careareas, proportionally to the priorities of the care areas or patterndensities of the care areas as discussed before. If the defects ofinterest received from the feedback path are validated to be real, theycan be ignored because it shows that the defect screening model hasperformed correctly. However, if the defects of interest are validatedto be nuisances, it would be preferable to include them in the modeltraining data to enhance the generated defect screening model.

As shown in FIG. 2, the validated and labelled data samples are storedin the defect/nuisance library 200 and used as the model training data202 and model validation data 203. It should be noted that the trainingdata and model manager 303 manages the data stored in thedefect/nuisance library. It has been known that if the number of datasamples in the model training data 202 is too large, the trained modelmay be over fit. Therefore, the training data and model manager 303keeps a proper number of defects or nuisances stored in thedefect/nuisance library by removing redundant data if necessary. Forexample, feature correlations between data samples may be computed andnew data samples highly correlated with existing data samples can beeliminated.

In order to achieve optimal performance of the defect screening model,training data and model manager 303 in the adaptive model controller 201also determines when the defect screening model should be updated. Thedefect screening model may be updated periodically or based on someother criteria. For example, if the SEM validation results show thatdefects of interests received in the feedback loop has been deviatedfrom the target specification, the defect screening models need to beupdated.

According to the present invention, a critical signature library 400 canbe established and updated for the adaptive machine learning 104 asshown in FIG. 4. The data modeling analytics 204 of FIG. 2 performs thetasks of critical signature analytics 504 for critical defects as shownin FIG. 5. The data models generated in association with the criticaldefects are the critical signature models 505 that are used by theadaptive machine learning 104 for auto defect screening 106. Thecritical signature models along with the associated circuit patterns,design clips, defect features, optical patches, SEM images are saved andupdated in a corresponding critical signature database 601 of thecritical signature library 400 as shown in FIG. 6.

The critical signature library 400 is a storage device configured tostore a library of critical signature databases 601 as shown in FIG. 6.In the critical signature library 400, various indexes may be used toindex each critical signature database 601. For example, the databasemay be indexed by technology nodes such as 14 nm, 10 nm or 7 nmtechnology node, or indexed by manufacturing lines, etc. Each criticalsignature database 601 includes a plurality of known critical circuitpatterns along with their corresponding data and critical signaturemodels.

In the present invention, each critical signature database 601 includesone or more data models generated as one or more critical signaturemodels by the critical signature analytics 504 in the adaptive machinelearning 104. Multiple data models may be established and saved for acorresponding critical signature database 601 by using differentmodeling algorithms or different sets of features extracted from thedesign clips or optical patches of the critical defects.

It should also be noted that the gist of the present invention resideson modeling the effect of the semiconductor manufacturing process on thecircuit patterns that result in defects with data models based onfeatures extracted from the design clips or corresponding opticalpatches. A good data model can be established only if the features usedin the data modeling can capture the effect of the semiconductormanufacturing process on the circuit patterns.

It has been well known and observed that optical proximity effect playsan important role in patterning the chip design layout. In order toimprove the accuracy and thoroughness of the established data models,the features used in the data modeling analytics 204 of the presentinvention for generating the data models 205 may include featuresextracted from design clips of different sizes for the circuit patternsassociated with each defect. By having different sizes of circuitpatterns, the optical proximity effect can better be captured in thedata models.

Because the circuit patterns are stacked layer by layer in manufacturingthe semiconductor device, in addition to using circuit patterns ofdifferent sizes for feature extraction, the present invention also usesdesign clips of the layers underneath the current design layer forextracting features to capture the effects of multiple circuit layers.Boolean operators such as OR, Exclusive OR, AND, NOT, etc., can beapplied to the design clips including the current layer and underneathlayers to form a composite circuit pattern for feature extraction.

FIG. 7 shows a flow chart summarizing the method for auto defectscreening using adaptive machine learning according to the presentinvention. The method collects a data set including a plurality ofdefect candidates in wafer inspection and defects of interest alreadyscreened by one or more existing defect screening models in step 701.The defect candidates are collected from wafer inspection before theyare screened by the existing defect screening models. The defects ofinterest are the screened results after auto defect screening withprevious wafer inspection.

Data samples in the data set including sampled defect candidates anddefects of interest are validated as being real defects or nuisances byusing SEM review/inspection and then used to update the data samplesstored in the defect/nuisance library for data modeling analytics instep 702.

Model training and validation data are compiled in step 703. One or moredata models are generated by the data modeling analytics as the updateddefect screening models based on features extracted from the dataassociated with the data samples in the model training data, and furthervalidated to meet a target specification by the model validation data instep 703.

As described before, the method of auto defect screening using adaptivemachine learning can improve the effectiveness of defect screening byusing defect screening models adaptive to possible process windowvariation. The defect candidates provided in the feed-forward pathensure that new defect types or nuisance natures are taken into accountfor updating the defect screening models. The defects of interest in thefeedback path checks if the defect screening model is satisfactory andnuisances slipped through the defect screening model are furtherincorporated in the model training data to update and improve the defectscreening model.

It may be worth mentioning that the adaptive machine learning as shownin FIGS. 2, 3, 4 and 5 in the present invention can be implemented by acomputing system that has one or more computing processors incooperation with one or more memory devices configured to executeprogram instructions designed to perform the functions of the adaptivemodel controller 201, data modeling analytics 204, defect sampler 301,SEM interface 302 and training data and model manager 303. Dedicatedhardware devices designed to deliver the required functionalities canalso be used instead of a general purpose computing system. Thedefect/nuisance library 200 and the critical signature library 400 canbe constructed using the memory devices controlled by the computingprocessors.

Although the present invention has been described with reference to thepreferred embodiments thereof, it is apparent to those skilled in theart that a variety of modifications and changes may be made withoutdeparting from the scope of the present invention which is intended tobe defined by the appended claims.

What is claimed is:
 1. A system for auto defect screening inmanufacturing a semiconductor device, said system having one or morecomputing processors and one or more memory devices configured andprogrammed to perform functional modules comprising: an adaptive modelcontroller including a defect sampler having a feed-forward inputreceiving a plurality of defect candidates acquired in inspecting one ormore wafers of said semiconductor device and a feedback input receivingdefects of interest already screened by using one or more existingdefect screening models, a scanning electron microscope (SEM) interfacereceiving defect information of data samples selected from saidplurality of defect candidates and said defects of interest andinterfacing with a SEM review/inspection tool to acquire correspondingSEM results of said data samples, and a training data and model managerreceiving said data samples and corresponding SEM results and outputtingmodel training data and model validation data; a data modeling analyticsexecutor receiving said model training data and said model validationdata and generating one or more updated defect screening models for autodefect screening from said model training data to satisfy a targetspecification validated with said model validation data; and an autodefect screener using said one or more updated defect screening modelsto predict if a defect candidate is a real defect or nuisance and filterout the predicted nuisance; wherein each of said data samples isvalidated and labelled as being a real defect or nuisance in saidcorresponding SEM results, and said adaptive model controller controlswhen to generate said one or more updated defect screening modelsaccording to a pre-set criteria.
 2. The system as claimed in claim 1,wherein said adaptive model controller further interfaces with adefect/nuisance library in which data samples forming said modeltraining data and said model validation data are saved and updated. 3.The system as claimed in claim 1, wherein a certain percentage of saidplurality of defect candidates received from said feed-forward input arerandomly and uniformly selected for said data samples.
 4. The system asclaimed in claim 1, wherein said plurality of defect candidates receivedfrom said feed-forward input are selected for said data samplesaccording to priorities of care areas set up in inspecting one or morewafers of said semiconductor device.
 5. The system as claimed in claim1, wherein said plurality of defect candidates received from saidfeed-forward input are selected for said data samples according topattern densities of one or more design clips associated with each ofsaid plurality of defect candidates in chip design layout of saidsemiconductor device.
 6. The system as claimed in claim 1, wherein eachdefect in said defects of interest received from said feedback input isignored if the defect is validated as being real in the correspondingSEM results.
 7. The system as claimed in claim 1, wherein said adaptivemodel controller controls when to generate said one or more updateddefect screening models according to validated real defects andnuisances in the corresponding SEM results of said defects of interestreceived from said feedback input with reference to said targetspecification.
 8. The system as claimed in claim 1, wherein said datamodeling analytics executor extracts a set of features associated witheach data sample in said model training data and generates said one ormore updated defect screening models based on said set of features, andsaid auto defect screener extracts said set of features associated witheach defect candidate to predict if the defect candidate is a realdefect or nuisance using said one or more updated defect screeningmodels.
 9. The system as claimed in claim 8, wherein said data modelinganalytics executor further interfaces with a critical signature libraryand one or more data models are generated by said data modelinganalytics executor and saved as one or more critical signature models ina corresponding signature database in said critical signature librarywith each critical signature model being generated by a different set offeatures.
 10. The system as claimed in claim 8, wherein said datamodeling analytics executor further interfaces with a critical signaturelibrary and one or more data models are generated by said data modelinganalytics executor and saved as one or more critical signature models ina corresponding signature database in said critical signature librarywith each critical signature model being generated by a different datamodeling algorithm.
 11. The system as claimed in claim 8, wherein saidset of features associated with each data sample in said model trainingdata includes features extracted from one or more design clips in chipdesign layout of said semiconductor device associated with the datasample.
 12. The system as claimed in claim 11, wherein said one or moredesign clips in said chip design layout includes at least one designclip of an underneath layer of a current layer of a wafer from which theassociated data sample is acquired.
 13. A method for auto defectscreening in manufacturing a semiconductor device using a system havingone or more computing processors and one or more memory devicesconfigured and programmed to function as an adaptive model controller, adata modeling analytics executor and an auto defect screener, saidmethod comprising the steps of: preparing a feed-forward input for saidadaptive model controller to receive a plurality of defect candidatesacquired from inspecting one or more wafers of said semiconductor deviceand a feedback input for said adaptive model controller to receivedefects of interest already screened by using one or more existingdefect screening models; selecting data samples from said plurality ofdefect candidates and said defects of interest in said adaptive modelcontroller and interfacing with a scanning electron microscope (SEM)review/inspection tool to acquire corresponding SEM results of said datasamples, each of said data samples being validated and labelled as beinga real defect or nuisance in said corresponding SEM results; compilingmodel training data and model validation data from accumulated datasamples in said adaptive model controller according to the correspondingSEM results of the accumulated data samples; using said data modelinganalytics executor to perform data modeling analytics to generate one ormore updated defect screening models from said model training data tosatisfy a target specification validated with said model validationdata; and using said one or more updated defect screening models in saidauto defect screener to predict if a defect candidate is a real defector nuisance and filter out the predicted nuisance; wherein said methodfurther uses said adaptive model controller to control when to generatesaid one or more updated defect screening models according to a pre-setcriteria.
 14. The method as claimed in claim 13, wherein said adaptivemodel controller further interfaces with a defect/nuisance library inwhich data samples forming said model training data and said modelvalidation data are saved and updated.
 15. The method as claimed inclaim 13, wherein a certain percentage of said plurality of defectcandidates received from said feed-forward input are randomly anduniformly selected for said data samples.
 16. The method as claimed inclaim 13, wherein said plurality of defect candidates received from saidfeed-forward input are selected for said data samples according topriorities of care areas set up in inspecting one or more wafers of saidsemiconductor device.
 17. The method as claimed in claim 13, whereinsaid plurality of defect candidates received from said feed-forwardinput are selected for said data samples according to pattern densitiesof one or more design clips associated with each of said plurality ofdefect candidates in chip design layout of said semiconductor device.18. The method as claimed in claim 13, wherein each defect in saiddefects of interest received from said feedback input is ignored if thedefect is validated as being real in the corresponding SEM results. 19.The method as claimed in claim 13, wherein said adaptive modelcontroller controls when to generate said one or more updated defectscreening models according to validated real defects and nuisances inthe corresponding SEM results of said defects of interest received fromsaid feedback input with reference to said target specification.
 20. Themethod as claimed in claim 13, wherein said data modeling analyticsexecutor extracts a set of features associated with each data sample insaid model training data and generates said one or more updated defectscreening models based on said set of features, and said auto defectscreener extracts said set of features associated with each defectcandidate to predict if the defect candidate is a real defect ornuisance using said one or more updated defect screening models.
 21. Themethod as claimed in claim 20, wherein said data modeling analyticsexecutor further interfaces with a critical signature library and one ormore data models are generated by said data modeling analytics executorand saved as one or more critical signature models in a correspondingsignature database in said critical signature library with each criticalsignature model being generated by a different set of features.
 22. Themethod as claimed in claim 20, wherein said data modeling analyticsexecutor further interfaces with a critical signature library and one ormore data models are generated by said data modeling analytics executorand saved as one or more critical signature models in a correspondingsignature database in said critical signature library with each criticalsignature model being generated by a different data modeling algorithm.23. The method as claimed in claim 20, wherein said set of featuresassociated with each data sample in said model training data includesfeatures extracted from one or more design clips in chip design layoutof said semiconductor device associated with the data sample.
 24. Themethod as claimed in claim 23, wherein said one or more design clips insaid chip design layout includes at least one design clip of anunderneath layer of a current layer of a wafer from which the associateddata sample is acquired.